Acute mental health concerns in emergency settings: development and validation of an Ovid MEDLINE search filter
Bibliographic record
Abstract
Background: The authors sought to develop and validate a search filter to retrieve research about acute mental health concerns during public health emergencies. They did so as a response to a recommendation from a previously published paper on searching for evidence in emergency contexts. Methods: The definition of acute mental health was adapted from the DSM-5 and the DynaMed entries on acute stress and posttraumatic stress disorder. The definition of public health emergencies was adapted from the Canadian Medical Protective Association. The authors retrieved systematic reviews on mental health concerns pertaining to people in the community and healthcare workers during public health emergencies from MEDLINE. The authors formulated gold standard sets for each population group using articles included in these reviews. The authors then separated the articles into development and validation sets. Keywords and Medical Subject Heading (MeSH) terms from the title and abstracts in the Ovid records in the development sets were used to formulate the filter. The filter was tested via the relative recall method using the validation sets. The authors then tested the filter for precision by conducting MEDLINE (Ovid) searches for the following topics for acute mental health: (i) children/adolescents and earthquakes; (ii) children/adolescents and Ebola outbreaks; (iii) healthcare workers and earthquakes; and (iv) healthcare workers and Ebola outbreaks. Results: The MEDLINE filter demonstrated 100% recall against the people in the community validation set and 98% recall against the healthcare worker validation set. The filter demonstrated the following percentages for the precision tests: (i) 94% for children/adolescents and earthquakes; (ii) 81% for children/adolescents and Ebola outbreaks; (iii) 81% for healthcare workers and earthquakes; and (iv) 79% for healthcare workers and Ebola outbreaks. Conclusion: The authors developed a validated search filter that could be used to find evidence related to acute mental health concerns in public health emergencies. The authors recommend that researchers adapt and modify the search filter to reflect the unique mental health issues of their population groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.229 | 0.553 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.048 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".